AI-generated videos often look distorted not because the models are flawed, but because of how they’re used. Complex prompts—those packed with multiple subjects, detailed text, or layered actions—overwhelm these tools, producing unstable results.
The solution lies in refining input rather than waiting for better models. Simplifying scenes, limiting objects, and running multiple generations can dramatically cut errors without requiring new software releases.
Strengths: when prompts work
video generators excel at single-subject scenes with clear details. A prompt like A small brown cat nudging a stuffed squirrel in a bright living room produces stable, natural-looking results. The key is specificity: listing exact appearance traits, lighting conditions, and camera angles reduces ambiguity for the model.
Running multiple generations also helps. Since these tools aren’t deterministic, identical prompts can yield vastly different outputs. Testing five to ten variations often uncovers at least one usable clip, even if early attempts fail.
Caveats: where limits remain
Text remains a weak point. Longer phrases or dynamic text (e.g., subtitles) frequently break rendering, leaving garbled or incomplete letters. Similarly, prompts with multiple people or objects increase the risk of body merging or sudden disappearances.
Longer sequences—those describing full narratives like ‘a person walks through a street and enters a café’—are particularly problematic. AI models struggle to maintain consistency across multiple actions, often scrambling order or distorting transitions.
Market impact
For enterprise buyers, upgrade timing hinges on prompt discipline rather than model performance. Current tools like Sora or Veo already deliver near-cinematic quality when used correctly. The barrier isn’t capability—it’s workflow. Teams should prioritize training on stable prompting techniques before investing in new hardware to avoid wasted spend.
